The adoption of AI-enabled business solutions continues to grow popularly across the tech industry. In post on the GPT-3 built by Open AI, we highlighted as an example of how AI might change the customer support sector.

This is just one of the examples of the sectors the innovation of artificial intelligence could potentially disrupt.

However, admittedly, the excitement around the innovation from AI runs into gridlocks as businesses seeking to execute their ideas run into data problems. In a number of cases, there seems to be a continuous gap between our brilliant ideas and execution. This article seeks to address what we consider as the missing link to executing AI into efficient business models that will profit everyone involved from the businesses to the end-consumer.

What we consider as the missing link is data preparation. Data Preparation is a significant step in the creation of any AI/ML project. It is vital to note that data preparation is quite synonymous with other phrases like data wrangling, data preprocessing. In essence, this process is important for the success of any AI/ML project.

The Missing Link: Idea and Execution

Every new machine learning algorithm is a unique process. This is the case for many reasons. One, the data being used to train the models may be different, two, the objective for the respective ML algorithm may differ from other models that have been built. However, what stands out is the need for good, quality data in the steps to building an ML model that will achieve its purpose. Indeed, many businesses are interested in the low-hanging fruits of AI as shown by many reports. However, the majority of these businesses also report that executing AI projects run into challenges such as data quality, labeling, and building model confidence.

From the Dimensional Research cited above, close to 96% of enterprises face the data quality and labeling challenges.

Finding the Missing Link: Data Preparation Techniques

The stage of data preparation is important to the execution of your ML project. There are a number of recommendations on how to efficiently prepare data for your ML project. This article will also make some recommendations on how to optimize data for your next big idea.

Raw data in many cases whether generated by an enterprise itself or obtained from a data lake, need to be cleaned up. The objective of preparing the data optimizes it for usage by the ML model. There are several activities involved in data preparation. Some of these steps can be identified as data cleaning, data selection, data transformation, feature engineering, and dimensionality reduction.

However, the data preparation process can be simplified into 3 steps: data selection, data pre-processing, and data transformation.

Data Selection

Selecting the data is important as not all data can or should be used for your ML model. The data selected has to be relevant to the problem you are seeking to solve. Many researchers and data scientists, therefore, recommend understanding the problem first. Another recommendation that comes in handy is concerning making assumptions about your data. Assumptions made about your data should be noted so these can be tested subsequently. While selecting data, teams need to determine the quantity of the data they have available. It is also important to note the desirable data which may currently be unavailable. This will enable the data scientist to detect whatever inadequacies may be addressed in the ML model at its outcome. After selecting the data, the next step is to pre-process the data.

Data Pre-processing

Pre-processing data is important for the way it will be used in the model. This is where you get the data into a format where it is useful for your model. First comes the formatting. The data needs to be in a suitable format, whether relational or in a text format, or whatever format that is suitable for your model. After the data has been placed in a proper format, the data is cleaned. That is, where there are missing values or certain variables are properly described do not properly reflect the task of the ML, they are manually optimized. At this point, data masking is also essential in order to stay within sensitive information guidelines. After the pre-processing is done, the data can then be transformed using some techniques.

Data Transformation

Data transformation can simply be described as feature engineering. The ML Engineer should understand the ML algorithm and have domain knowledge of the sector that the model addresses. Some popular forms of feature engineering include scaling, decomposition, and aggregation. Feature engineering helps engineering teams understand their data better and create effective ML models.

Tools and Frameworks for Data Preparation

The use of tools and frameworks in the process of data preparation is largely dependent on the nature of data and the level of team expertise. Therefore, the tools and frameworks used in an image processing model may not be the best to use in an audio processing model. However, in many cases, engineers and data scientists have highly recommended popular solutions like Tensorflow, PyTorch, Keras and Sckit Learn as great ML frameworks for data preparation and classification.

The tools vary in their effective usage and types of algorithms they can be useful for. It is important for your team to clarify which tools best work with the team objective. For example, KNIME is a tool which is more effective for large data volumes and supports projects around text and image mining through plugins. Scikit Learn is also another tool which supports features such as classification, clustering, and preprocessing, making it an impressive choice when there would be a lot of data processing before the ML model is built.

Another popular tool is Weka which is built on Java code and can be used for a series of activities such as data preparation, classification, clustering, and visualization. Many of these tools are free and provide help in data mining and analysis. However, while some have a great documentation process, some other tools are harder to learn. So it is important to understand the complexity of some models and how they can be useful for your team.